Retrospective cohort analysis is a widely used technique in many fields. In the medical field, for example, electronic medical records (EMRs) contain a large amount of medical information for patients. It is often desirable to group similar patients as a cohort. Patient cohorts are groups of patients and their associated information, such as gender, age, diagnoses, and treatments. Retrospective patient cohort analysis is the analysis of medical and diagnostic histories of similar patients to make healthcare discoveries.
In the traditional pipeline, analysts work manually to define specific cohort constraints (e.g., “female patients over age 70”) or apply specialized batch analytics to computationally determine a meaningful group of patients (e.g., high-utilization cohorts). Unfortunately, both methods have limitations. For the definition of the cohort constraints, it is difficult to select the attributes that are to be queried from a list of hundreds or thousands of patient attributes. For batch analytics that behave like a “black box,” users have few ways to apply their domain expertise to influence the process.
A need exists for an integrated system that combines visual exploration and data analytics to interactively visualize and refine cohorts, request analytics on those cohorts, and make new discoveries.